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<li class="navelem"><b>nz</b></li><li class="navelem"><a class="el" href="namespacenz_1_1opt.html">opt</a></li><li class="navelem"><a class="el" href="classnz_1_1opt_1_1_ada_delta.html">AdaDelta</a></li>  </ul>
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<p><a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimizer for deep learning models.  
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-methods" name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a7bf0e669daf70a8de09d3e1c9cb8bc5c" id="r_a7bf0e669daf70a8de09d3e1c9cb8bc5c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="#a7bf0e669daf70a8de09d3e1c9cb8bc5c">AdaDelta</a> (Tensor::value_type rho)</td></tr>
<tr class="memdesc:a7bf0e669daf70a8de09d3e1c9cb8bc5c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructs an <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimizer with a specified decay rate.  <br /></td></tr>
<tr class="separator:a7bf0e669daf70a8de09d3e1c9cb8bc5c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0d24bd903517823f9607160d2e8207a1" id="r_a0d24bd903517823f9607160d2e8207a1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="#a0d24bd903517823f9607160d2e8207a1">step</a> (<a class="el" href="classnz_1_1nodes_1_1_node.html">Node</a> *input) override</td></tr>
<tr class="memdesc:a0d24bd903517823f9607160d2e8207a1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Performs a single optimization step using the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> algorithm.  <br /></td></tr>
<tr class="separator:a0d24bd903517823f9607160d2e8207a1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classnz_1_1opt_1_1_optimizer"><td colspan="2" onclick="javascript:dynsection.toggleInherit('pub_methods_classnz_1_1opt_1_1_optimizer')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classnz_1_1opt_1_1_optimizer.html">nz::opt::Optimizer</a></td></tr>
<tr class="memitem:aaf8d92566a815254dbb0ace9af9cb1ae inherit pub_methods_classnz_1_1opt_1_1_optimizer" id="r_aaf8d92566a815254dbb0ace9af9cb1ae"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnz_1_1opt_1_1_optimizer.html#aaf8d92566a815254dbb0ace9af9cb1ae">Optimizer</a> ()=default</td></tr>
<tr class="memdesc:aaf8d92566a815254dbb0ace9af9cb1ae inherit pub_methods_classnz_1_1opt_1_1_optimizer"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor for the <a class="el" href="classnz_1_1opt_1_1_optimizer.html" title="Base class for optimization algorithms in deep learning.">Optimizer</a> class.  <br /></td></tr>
<tr class="separator:aaf8d92566a815254dbb0ace9af9cb1ae inherit pub_methods_classnz_1_1opt_1_1_optimizer"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab9262983ef3bd11e6f548862b2f58e1d inherit pub_methods_classnz_1_1opt_1_1_optimizer" id="r_ab9262983ef3bd11e6f548862b2f58e1d"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnz_1_1opt_1_1_optimizer.html#ab9262983ef3bd11e6f548862b2f58e1d">~Optimizer</a> ()=default</td></tr>
<tr class="memdesc:ab9262983ef3bd11e6f548862b2f58e1d inherit pub_methods_classnz_1_1opt_1_1_optimizer"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default destructor for the <a class="el" href="classnz_1_1opt_1_1_optimizer.html" title="Base class for optimization algorithms in deep learning.">Optimizer</a> class.  <br /></td></tr>
<tr class="separator:ab9262983ef3bd11e6f548862b2f58e1d inherit pub_methods_classnz_1_1opt_1_1_optimizer"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimizer for deep learning models. </p>
<p>The <code><a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a></code> class implements the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimization algorithm, which is a variant of the Adagrad optimizer that seeks to reduce its aggressive, monotonically decreasing learning rate. Instead of accumulating all past squared gradients, <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> restricts the window of accumulation to a fixed size, allowing for more robust updates and addressing the diminishing learning rate problem.</p>
<p>This class extends the <code><a class="el" href="classnz_1_1opt_1_1_optimizer.html" title="Base class for optimization algorithms in deep learning.">Optimizer</a></code> base class and provides a concrete implementation of the <code>step</code> method, which updates the model's parameters (represented as <code>Node</code> objects) using the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> algorithm.</p>
<ul>
<li>The optimizer maintains two accumulators for each parameter (<code>Node</code>):<ul>
<li>( E[g^2]_t ): The exponentially decaying average of past squared gradients.</li>
<li>( E[\Delta x^2]_t ): The exponentially decaying average of past squared parameter updates.</li>
</ul>
</li>
<li>The accumulators are updated using the following formulas: [ E[g^2]_t = \rho E[g^2]_{t-1} + (1 - \rho) g_t^2 ] [ \Delta x_t = - \frac{\sqrt{E[\Delta x^2]_{t-1} + \epsilon}}{\sqrt{E[g^2]_t + \epsilon}} g_t ] [ E[\Delta x^2]_t = \rho E[\Delta x^2]_{t-1} + (1 - \rho) \Delta x_t^2 ] where ( g_t ) is the current gradient, ( \rho ) is the decay rate, and ( \epsilon ) is a small constant to prevent division by zero.</li>
<li>The model parameters are updated using ( \Delta x_t ), which is computed adaptively based on the ratio of the two accumulators.</li>
<li>The optimizer uses GPU-accelerated computations through CUDA to efficiently update parameters, making it suitable for large-scale models.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd><ul>
<li>The optimizer assumes that the model parameters are represented by <code>Node</code> objects, and each node must have associated gradients.</li>
<li>The accumulators (<code>acc_grad</code> and <code>acc_delta</code>) are stored per <code>Node</code> object. If a <code>Node</code> does not have existing accumulators, they are initialized to zero tensors.</li>
<li>The optimizer utilizes GPU memory for accumulator storage and gradient computation, requiring CUDA support.</li>
<li>Ensure that the model parameters have been properly initialized, and gradients are computed before calling this method.</li>
</ul>
</dd></dl>
<h3><a class="anchor" id="autotoc_md121"></a>
Usage Example:</h3>
<div class="fragment"><div class="line"><a class="code hl_class" href="classnz_1_1opt_1_1_ada_delta.html">AdaDelta</a> optimizer(0.95); <span class="comment">// rho = 0.95</span></div>
<div class="line">graph.update(&amp;optimizer); <span class="comment">// Suppose &quot;graph&quot; is a computation graph waiting for gradient updates.</span></div>
<div class="ttc" id="aclassnz_1_1opt_1_1_ada_delta_html"><div class="ttname"><a href="classnz_1_1opt_1_1_ada_delta.html">nz::opt::AdaDelta</a></div><div class="ttdoc">AdaDelta optimizer for deep learning models.</div><div class="ttdef"><b>Definition</b> <a href="_optimizer_8cuh_source.html#l00987">Optimizer.cuh:987</a></div></div>
</div><!-- fragment --><dl class="section see"><dt>See also</dt><dd><a class="el" href="classnz_1_1opt_1_1_optimizer.html" title="Base class for optimization algorithms in deep learning.">Optimizer</a> for the base class that defines the interface for all optimizers. </dd>
<dd>
Nodes::Node for the class representing model parameters.</dd></dl>
<dl class="section author"><dt>Author</dt><dd>Mgepahmge (<a href="https://github.com/Mgepahmge">https://github.com/Mgepahmge</a>)</dd></dl>
<dl class="section date"><dt>Date</dt><dd>2024/12/07 </dd></dl>

<p class="definition">Definition at line <a class="el" href="_optimizer_8cuh_source.html#l00987">987</a> of file <a class="el" href="_optimizer_8cuh_source.html">Optimizer.cuh</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a7bf0e669daf70a8de09d3e1c9cb8bc5c" name="a7bf0e669daf70a8de09d3e1c9cb8bc5c"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a7bf0e669daf70a8de09d3e1c9cb8bc5c">&#9670;&#160;</a></span>AdaDelta()</h2>

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          <td class="memname">nz::opt::AdaDelta::AdaDelta </td>
          <td>(</td>
          <td class="paramtype">Tensor::value_type</td>          <td class="paramname"><span class="paramname"><em>rho</em></span></td><td>)</td>
          <td></td>
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<p>Constructs an <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimizer with a specified decay rate. </p>
<p>Initializes the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimization algorithm with a given decay rate (rho). <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> is an adaptive learning rate method that automatically adjusts the learning rate for each parameter, addressing some limitations of traditional stochastic gradient descent methods.</p>
<p>Unlike other adaptive optimization algorithms, <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> does not require an explicit learning rate. Instead, it uses a running average of squared gradients and squared parameter updates to scale the optimization step dynamically.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">rho</td><td>The decay rate that controls the moving window for accumulating gradient statistics. This parameter determines how quickly the algorithm forgets past gradient information. Typically set between 0.9 and 0.999.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd><ul>
<li>The <code>rho</code> parameter is analogous to the momentum decay rates in other adaptive optimization algorithms.</li>
<li>A value closer to 1 results in a longer memory of past gradients, while a value closer to 0 makes the algorithm more responsive to recent gradients.</li>
<li>Default recommended value is often around 0.95.</li>
</ul>
</dd></dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classnz_1_1opt_1_1_r_m_sprop.html" title="RMSprop optimizer for deep learning models.">RMSprop</a>, <a class="el" href="classnz_1_1opt_1_1_adam.html" title="Adam optimizer for deep learning models.">Adam</a> Alternative adaptive optimization algorithms </dd>
<dd>
<a class="el" href="#a0d24bd903517823f9607160d2e8207a1" title="Performs a single optimization step using the AdaDelta algorithm.">AdaDelta::step</a> Method that applies the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> update rule</dd></dl>
<dl class="section author"><dt>Author</dt><dd>Mgepahmge (<a href="https://github.com/Mgepahmge">https://github.com/Mgepahmge</a>)</dd></dl>
<dl class="section date"><dt>Date</dt><dd>2024/12/07 </dd></dl>

<p class="definition">Definition at line <a class="el" href="_optimizer_8cu_source.html#l00136">136</a> of file <a class="el" href="_optimizer_8cu_source.html">Optimizer.cu</a>.</p>

</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a id="a0d24bd903517823f9607160d2e8207a1" name="a0d24bd903517823f9607160d2e8207a1"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a0d24bd903517823f9607160d2e8207a1">&#9670;&#160;</a></span>step()</h2>

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          <td class="memname">void nz::opt::AdaDelta::step </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classnz_1_1nodes_1_1_node.html">Node</a> *</td>          <td class="paramname"><span class="paramname"><em>input</em></span></td><td>)</td>
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<p>Performs a single optimization step using the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> algorithm. </p>
<p>This method updates the model parameters for a given input node using the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> optimization algorithm. It manages adaptive learning rates by maintaining running accumulators for both gradient and parameter update magnitudes.</p>
<p>The method performs several key operations:</p><ol type="1">
<li>Lazily initializes accumulators for parameter updates and gradients if they don't exist</li>
<li>Prepares CUDA grid and block configurations for parallel parameter updates</li>
<li>Invokes a CUDA kernel to apply the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> update rule</li>
</ol>
<p>The lazy initialization of accumulators ensures that each parameter has its own adaptive learning rate, allowing for more flexible and efficient optimization across different model parameters.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>A pointer to the <code>Node</code> object representing the model parameter to be updated. The node must have a valid output tensor and its gradient already computed.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section note"><dt>Note</dt><dd><ul>
<li>This method assumes the input node has a valid gradient stored in its output object.</li>
<li>Accumulators for parameter updates and gradients are created on-demand for each unique input node.</li>
<li>The method uses CUDA for parallel computation of parameter updates.</li>
<li>The algorithm adapts the learning rate based on the historical gradient information.</li>
</ul>
</dd></dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="#a7bf0e669daf70a8de09d3e1c9cb8bc5c" title="Constructs an AdaDelta optimizer with a specified decay rate.">AdaDelta::AdaDelta()</a> Constructor for initializing optimizer parameters </dd>
<dd>
<a class="el" href="namespacenz_1_1krnl.html#a1f71726879c2d6a9d790522cdc1576e1" title="Kernel function to apply AdaDelta optimization.">krnl::AdaDelta</a> CUDA kernel implementing the <a class="el" href="classnz_1_1opt_1_1_ada_delta.html" title="AdaDelta optimizer for deep learning models.">AdaDelta</a> update rule</dd></dl>
<dl class="section author"><dt>Author</dt><dd>Mgepahmge (<a href="https://github.com/Mgepahmge">https://github.com/Mgepahmge</a>)</dd></dl>
<dl class="section date"><dt>Date</dt><dd>2024/12/07 </dd></dl>

<p>Implements <a class="el" href="classnz_1_1opt_1_1_optimizer.html#a826381abaaf29dbebade7cfd38b266e4">nz::opt::Optimizer</a>.</p>

<p class="definition">Definition at line <a class="el" href="_optimizer_8cu_source.html#l00140">140</a> of file <a class="el" href="_optimizer_8cu_source.html">Optimizer.cu</a>.</p>
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<hr/>The documentation for this class was generated from the following files:<ul>
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<li>D:/Users/Mgepahmge/Documents/C Program/NeuZephyr/src/<a class="el" href="_optimizer_8cu_source.html">Optimizer.cu</a></li>
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